This is an result for the gene singatures as well as assigned cell type for each cluster based on Danaher et al.
knitr::opts_chunk$set(cache=TRUE)
for( i in 1:length(mean_score[1,])){
print(visualize_me(mean_score[,i],cell_list[i],analysis_results$tsne[c("TSNE.1","TSNE.2")],title=names(cell_list)[i]))
# really hard to see
#print(plot(mean_score[,i],type = "h",xlab="Single Cell",ylab=colnames(mean_score)[i]))
h <-hist(mean_score[,i],plot = FALSE)
print(plot(h, freq = TRUE, labels =TRUE, ylim=c(0, 1.2*max(h$counts)),main=c(names(cell_list)[i]," Score per Single Cell"),xlab=paste(sep=" ",names(cell_list)[i],"Expression Level"),ylab="Numbers of cells"))
}
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sig_plot <- visualize_clusters(cluster_tsne$cell_type,cluster_tsne[c("TSNE.1","TSNE.2")],title="Danaher cell-type labels",legend_anno= sort(unique(cluster_tsne[,"name_type"])))
#sig_plot <-sig_plot +scale_fill_discrete(name="Cell Type",
# breaks=unique(cluster_assignment$cell_type),
# labels=sapply(unique(cluster_assignment$cell_type),function(x) names(cell_list[x])))
print(sig_plot)
counter <-1
colnames(score_by_cluster) <- names(cell_list)
apply(score_by_cluster, 1,function(x) {plot(x,ylab="mean expression level",xlab="cell type", main=c("Cluster",counter))
text(x, names(cell_list), cex=0.6, pos=4, col="red")
counter <<- counter + 1})
## [1] 2 3 4 5 6 7 8 9 10 11
This is a gene exprssion profile for each cell signature for each custer.The top expressed gene signatures of the majority of the cluster are well reprsented (All signatures are expressed among 80% to 90% of the cells in each cluster, except in Cluster 4,5 and 9)
top_sig <- data.frame(t(sapply(unique(all_type_expr_table$Cluster),function(x){ all_type_expr_table[all_type_expr_table$Cluster == x,][which(all_type_expr_table$precent_count[all_type_expr_table$Cluster == x] == max(all_type_expr_table$precent_count[all_type_expr_table$Cluster == x])),] })))
top_sig$Signature <-lapply(top_sig$Signature,function(y){paste(y)})
top_sig
## Cluster Signature Barcode_count total_Exp avg_non_zero
## 1 1 Macrophages 832 5571 5.435122
## 2 2 T_cells 11147 103645 4.072815
## 3 3 T_cells 16150 139048 3.920711
## 4 4 Neutrophils 546 1239 1.998387
## 5 5 Macrophages 34 177 5.205882
## 6 6 B_cells 2187 21816 4.088456
## 7 7 Cytotoxic_cells 5882 283649 7.918954
## 8 8 Cytotoxic_cells 183 1727 7.256303
## 9 9 Macrophages 10 16 1.6
## 10 10, 10 Macrophages, Mast_cells 1, 1 1, 9 1, 3
## SD cell_total_count precent_count
## 1 3.699126 963 86.39668
## 2 3.460277 11716 95.14339
## 3 3.382486 17068 94.62151
## 4 2.303681 1768 30.88235
## 5 3.20775 75 45.33333
## 6 3.623682 2282 95.83699
## 7 5.924657 5900 99.69492
## 8 4.297662 202 90.59406
## 9 1.897367 25 40
## 10 NA, 3.464102 1, 1 100, 100
all_type_expr_table
## Cluster Signature Barcode_count total_Exp avg_non_zero SD
## 1 1 B_cells 28 55 1.527778 2.360219
## 2 1 CD45 480 1681 3.502083 3.303695
## 3 1 CD8_T_cells 39 114 2.651163 2.869243
## 4 1 Cytotoxic_cells 303 1376 3.192575 4.026371
## 5 1 Exhausted_CD8 161 284 1.690476 1.945215
## 6 1 Macrophages 832 5571 5.435122 3.699126
## 7 1 Neutrophils 311 654 1.826816 2.112563
## 8 1 NK_CD56dim_cells 21 21 1.000000 0.000000
## 9 1 NK_cells 6 25 3.571429 3.207135
## 10 1 T_cells 143 638 2.913242 2.991848
## 11 1 Th1_cells 9 15 1.666667 2.000000
## 12 1 Treg 2 2 1.000000 0.000000
## 13 2 B_cells 340 795 2.048969 2.444246
## 14 2 CD45 3646 9692 2.658256 2.807309
## 15 2 CD8_T_cells 2649 11130 3.312500 3.172329
## 16 2 Cytotoxic_cells 7866 112444 5.236042 4.343983
## 17 2 DC 42 60 1.428571 1.563967
## 18 2 Exhausted_CD8 1778 3240 1.724321 1.991355
## 19 2 Macrophages 282 408 1.441696 1.613098
## 20 2 Mast_cells 3 10 3.333333 4.041452
## 21 2 Neutrophils 19 25 1.315789 1.376494
## 22 2 NK_CD56dim_cells 173 268 1.540230 1.778514
## 23 2 NK_cells 402 1033 2.275330 2.524312
## 24 2 T_cells 11147 103645 4.072815 3.460277
## 25 2 Th1_cells 305 408 1.337705 1.393378
## 26 2 Treg 84 163 1.940476 2.213601
## 27 3 B_cells 207 449 1.918803 2.297094
## 28 3 CD45 4863 11961 2.459593 2.661324
## 29 3 CD8_T_cells 4141 21377 3.926708 3.351746
## 30 3 Cytotoxic_cells 5187 13357 2.174345 2.566798
## 31 3 DC 8 8 1.000000 0.000000
## 32 3 Exhausted_CD8 1001 1495 1.477273 1.644003
## 33 3 Macrophages 248 321 1.294355 1.309436
## 34 3 Mast_cells 4 16 4.000000 3.464102
## 35 3 Neutrophils 31 38 1.187500 1.060660
## 36 3 NK_CD56dim_cells 137 173 1.262774 1.232346
## 37 3 NK_cells 28 51 1.593750 1.881307
## 38 3 T_cells 16150 139048 3.920711 3.382486
## 39 3 Th1_cells 64 82 1.281250 1.278252
## 40 3 Treg 30 55 1.833333 2.166888
## 41 4 B_cells 58 149 2.328125 2.678869
## 42 4 CD45 265 585 2.207547 2.528313
## 43 4 CD8_T_cells 41 94 2.136364 2.445603
## 44 4 Cytotoxic_cells 387 1881 3.329204 3.853424
## 45 4 DC 1 1 1.000000 NA
## 46 4 Exhausted_CD8 99 151 1.480392 1.657449
## 47 4 Macrophages 391 769 1.835322 2.167417
## 48 4 Neutrophils 546 1239 1.998387 2.303681
## 49 4 NK_CD56dim_cells 7 19 2.714286 2.927700
## 50 4 NK_cells 9 11 1.000000 0.000000
## 51 4 T_cells 259 719 2.152695 2.487936
## 52 4 Th1_cells 8 8 1.000000 0.000000
## 53 5 CD45 2 2 1.000000 0.000000
## 54 5 CD8_T_cells 1 7 7.000000 NA
## 55 5 Cytotoxic_cells 21 147 3.769231 4.836601
## 56 5 Macrophages 34 177 5.205882 3.207750
## 57 5 NK_cells 1 1 1.000000 NA
## 58 5 T_cells 6 35 3.181818 3.027150
## 59 5 Th1_cells 1 1 1.000000 NA
## 60 6 B_cells 2187 21816 4.088456 3.623682
## 61 6 CD45 539 1249 2.317254 2.580579
## 62 6 CD8_T_cells 59 149 2.191176 2.638732
## 63 6 Cytotoxic_cells 377 2065 3.542024 4.473811
## 64 6 Exhausted_CD8 59 77 1.305085 1.329431
## 65 6 Macrophages 82 116 1.397590 1.592205
## 66 6 Mast_cells 1 1 1.000000 NA
## 67 6 Neutrophils 20 20 1.000000 0.000000
## 68 6 NK_CD56dim_cells 20 20 1.000000 0.000000
## 69 6 NK_cells 5 5 1.000000 0.000000
## 70 6 T_cells 306 1375 2.846791 3.010584
## 71 6 Th1_cells 16 16 1.000000 0.000000
## 72 6 Treg 1 1 1.000000 NA
## 73 7 B_cells 74 270 2.477064 3.065931
## 74 7 CD45 2104 6099 2.898764 2.947910
## 75 7 CD8_T_cells 2505 10733 3.266281 3.112894
## 76 7 Cytotoxic_cells 5882 283649 7.918954 5.924657
## 77 7 DC 1 1 1.000000 NA
## 78 7 Exhausted_CD8 996 1769 1.662594 1.924216
## 79 7 Macrophages 92 151 1.641304 2.019583
## 80 7 Neutrophils 12 12 1.000000 0.000000
## 81 7 NK_CD56dim_cells 414 713 1.646651 1.908156
## 82 7 NK_cells 1052 2835 2.304878 2.589437
## 83 7 T_cells 3853 33071 4.141641 3.509735
## 84 7 Th1_cells 848 1558 1.837264 2.108493
## 85 7 Treg 7 7 1.000000 0.000000
## 86 8 B_cells 132 635 3.489011 3.303671
## 87 8 CD45 16 29 1.812500 2.227667
## 88 8 CD8_T_cells 6 18 3.000000 3.098387
## 89 8 Cytotoxic_cells 183 1727 7.256303 4.297662
## 90 8 Exhausted_CD8 3 3 1.000000 0.000000
## 91 8 Macrophages 59 170 2.575758 2.700773
## 92 8 Neutrophils 8 8 1.000000 0.000000
## 93 8 NK_CD56dim_cells 1 1 1.000000 NA
## 94 8 NK_cells 1 1 1.000000 NA
## 95 8 T_cells 35 176 3.591837 3.174891
## 96 8 Th1_cells 2 2 1.000000 0.000000
## 97 9 B_cells 1 1 1.000000 NA
## 98 9 Cytotoxic_cells 4 4 1.000000 0.000000
## 99 9 Macrophages 10 16 1.600000 1.897367
## 100 9 T_cells 2 2 1.000000 0.000000
## 101 10 Macrophages 1 1 1.000000 NA
## 102 10 Mast_cells 1 9 3.000000 3.464102
## cell_total_count precent_count
## 1 963 2.90758048
## 2 963 49.84423676
## 3 963 4.04984424
## 4 963 31.46417445
## 5 963 16.71858775
## 6 963 86.39667705
## 7 963 32.29491173
## 8 963 2.18068536
## 9 963 0.62305296
## 10 963 14.84942887
## 11 963 0.93457944
## 12 963 0.20768432
## 13 11716 2.90201434
## 14 11716 31.11983612
## 15 11716 22.61010584
## 16 11716 67.13895527
## 17 11716 0.35848412
## 18 11716 15.17582793
## 19 11716 2.40696483
## 20 11716 0.02560601
## 21 11716 0.16217139
## 22 11716 1.47661318
## 23 11716 3.43120519
## 24 11716 95.14339365
## 25 11716 2.60327757
## 26 11716 0.71696825
## 27 17068 1.21279588
## 28 17068 28.49191469
## 29 17068 24.26177642
## 30 17068 30.39020389
## 31 17068 0.04687134
## 32 17068 5.86477619
## 33 17068 1.45301148
## 34 17068 0.02343567
## 35 17068 0.18162644
## 36 17068 0.80267167
## 37 17068 0.16404968
## 38 17068 94.62151394
## 39 17068 0.37497071
## 40 17068 0.17576752
## 41 1768 3.28054299
## 42 1768 14.98868778
## 43 1768 2.31900452
## 44 1768 21.88914027
## 45 1768 0.05656109
## 46 1768 5.59954751
## 47 1768 22.11538462
## 48 1768 30.88235294
## 49 1768 0.39592760
## 50 1768 0.50904977
## 51 1768 14.64932127
## 52 1768 0.45248869
## 53 75 2.66666667
## 54 75 1.33333333
## 55 75 28.00000000
## 56 75 45.33333333
## 57 75 1.33333333
## 58 75 8.00000000
## 59 75 1.33333333
## 60 2282 95.83698510
## 61 2282 23.61963190
## 62 2282 2.58545136
## 63 2282 16.52059597
## 64 2282 2.58545136
## 65 2282 3.59333918
## 66 2282 0.04382121
## 67 2282 0.87642419
## 68 2282 0.87642419
## 69 2282 0.21910605
## 70 2282 13.40929010
## 71 2282 0.70113935
## 72 2282 0.04382121
## 73 5900 1.25423729
## 74 5900 35.66101695
## 75 5900 42.45762712
## 76 5900 99.69491525
## 77 5900 0.01694915
## 78 5900 16.88135593
## 79 5900 1.55932203
## 80 5900 0.20338983
## 81 5900 7.01694915
## 82 5900 17.83050847
## 83 5900 65.30508475
## 84 5900 14.37288136
## 85 5900 0.11864407
## 86 202 65.34653465
## 87 202 7.92079208
## 88 202 2.97029703
## 89 202 90.59405941
## 90 202 1.48514851
## 91 202 29.20792079
## 92 202 3.96039604
## 93 202 0.49504950
## 94 202 0.49504950
## 95 202 17.32673267
## 96 202 0.99009901
## 97 25 4.00000000
## 98 25 16.00000000
## 99 25 40.00000000
## 100 25 8.00000000
## 101 1 100.00000000
## 102 1 100.00000000
A closer look at gene composition in Cluster 7 (used as an example). PCA is performed for Cytotoxic_cells to determine which gene(s) “drive” the signature. Excluding cell with only zeor counts. Furhter Normalization is required - Need to work on that.
knitr::opts_chunk$set(cache=TRUE)
all_list_gene<- get_signature_matrix (all_type_expr,7,"Cytotoxic_cells")
alg_pca<-prcomp(all_list_gene)
summary(alg_pca)
## Importance of components%s:
## PC1 PC2 PC3 PC4 PC5 PC6 PC7
## Standard deviation 8.7936 5.8285 5.2541 4.9899 4.48346 3.9861 3.67443
## Proportion of Variance 0.3421 0.1503 0.1221 0.1102 0.08894 0.0703 0.05974
## Cumulative Proportion 0.3421 0.4925 0.6146 0.7248 0.81371 0.8840 0.94376
## PC8 PC9 PC10
## Standard deviation 3.45742 0.87048 0
## Proportion of Variance 0.05289 0.00335 0
## Cumulative Proportion 0.99665 1.00000 1
plot(alg_pca, type = "l")
alg_pca
## Standard deviations (1, .., p=10):
## [1] 8.7935689 5.8285039 5.2541072 4.9899298 4.4834628 3.9860782 3.6744318
## [8] 3.4574179 0.8704766 0.0000000
##
## Rotation (n x k) = (10 x 10):
## PC1 PC2 PC3 PC4
## ENSG00000172543 -0.050876357 0.064450843 -0.266535196 0.935181890
## ENSG00000115523 0.962513591 0.187070782 0.146121342 0.063901415
## ENSG00000145649 0.048840454 -0.044780733 -0.133907730 0.152381261
## ENSG00000100453 0.088441188 -0.125541620 -0.132864708 -0.026512722
## ENSG00000100450 0.050641375 -0.044374122 0.163583203 0.182051154
## ENSG00000180644 0.101475027 0.341239991 -0.856091055 -0.245492591
## ENSG00000105374 0.211639919 -0.904619540 -0.306574935 -0.030423399
## ENSG00000134539 0.055595212 -0.079572725 0.135507816 0.055429245
## ENSG00000111796 0.005285606 0.001572834 0.004616799 0.004609626
## ENSG00000213809 0.000000000 0.000000000 0.000000000 0.000000000
## PC5 PC6 PC7 PC8
## ENSG00000172543 0.1345975494 0.013434041 -0.148839962 -0.08478107
## ENSG00000115523 0.0811692159 0.058702898 -0.046504283 0.03015375
## ENSG00000145649 -0.0400913105 -0.058931103 0.818109881 0.52922009
## ENSG00000100453 -0.0152375302 -0.936348129 -0.216648707 0.18472484
## ENSG00000100450 -0.9657662849 0.007425380 -0.051226751 -0.01260386
## ENSG00000180644 -0.2006149910 0.049925164 0.034944441 -0.19007970
## ENSG00000105374 0.0002883222 0.188946442 -0.059107424 -0.05245696
## ENSG00000134539 0.0215394720 -0.278940546 0.502046872 -0.79905868
## ENSG00000111796 0.0063748678 -0.009826444 0.007316746 -0.01304229
## ENSG00000213809 0.0000000000 0.000000000 0.000000000 0.00000000
## PC9 PC10
## ENSG00000172543 -0.0036562987 0
## ENSG00000115523 -0.0055591476 0
## ENSG00000145649 0.0003209876 0
## ENSG00000100453 -0.0046448304 0
## ENSG00000100450 0.0046487401 0
## ENSG00000180644 0.0030463462 0
## ENSG00000105374 0.0034637069 0
## ENSG00000134539 -0.0180269393 0
## ENSG00000111796 0.9997830720 0
## ENSG00000213809 0.0000000000 1
test_1 <- all_type_expr[ all_type_expr$Cluster==7 & all_type_expr$Signature =="Cytotoxic_cells",] %>% group_by(Gene,Signature) %>% summarise(count=n_distinct(Barcode),EXP_mean=mean(as.numeric(Expression)),SD_mean = sd(as.numeric(Expression)),Abs_Exp=sum(as.numeric(Expression)))
test_1
## # A tibble: 10 x 6
## # Groups: Gene [?]
## Gene Signature count EXP_mean SD_mean Abs_Exp
## <fctr> <fctr> <int> <dbl> <dbl> <dbl>
## 1 ENSG00000100450 Cytotoxic_cells 4090 6.596822 3.684426 26981
## 2 ENSG00000100453 Cytotoxic_cells 4872 7.262931 3.795214 35385
## 3 ENSG00000105374 Cytotoxic_cells 5845 9.955004 5.375419 58187
## 4 ENSG00000111796 Cytotoxic_cells 411 2.289538 2.588221 941
## 5 ENSG00000115523 Cytotoxic_cells 5719 14.044938 8.436785 80323
## 6 ENSG00000134539 Cytotoxic_cells 1395 2.318280 2.553963 3234
## 7 ENSG00000145649 Cytotoxic_cells 4982 6.480329 3.599158 32285
## 8 ENSG00000172543 Cytotoxic_cells 4851 5.998557 3.573586 29099
## 9 ENSG00000180644 Cytotoxic_cells 3647 4.718124 3.577074 17207
## 10 ENSG00000213809 Cytotoxic_cells 7 1.000000 0.000000 7